Effectiveness of CASE Ultra Expert System in Evaluating Adverse Effects of Drugs.

Purpose of this pilot study is to test the QSAR expert system CASE Ultra for adverse effect prediction of drugs. 870 drugs from the SIDER adverse effect dataset were tested using CASE Ultra for carcinogenicity, genetic, liver, cardiac, renal and reproductive toxicity. 47 drugs that were withdrawn from market since the 1950s were also evaluated for potential risks using CASE Ultra and compared them with the actual reasons for which the drugs were recalled. For the whole SIDER test set (n=870), sensitivity and specificity of the carcinogenicity predictions are 66.67 % and 82.17 % respectively; for liver toxicity: 78.95 %, 78.50 %; cardiotoxicity: 69.07 %, 57.57 %; renal toxicity: 46.88 %, 67.90 %; and reproductive toxicity: 100.00 %, 61.10 %. For the SIDER test chemicals not present in the training sets of the models, sensitivity and specificity of carcinogenicity predictions are 100.00 % and 88.89 % respectively (n=404); for liver toxicity: 100.00 %, 51.33 % (n=115); cardiotoxicity: 100.00 %, 20.45 % (n=94); renal toxicity: 100.00 %, 45.54 % (n=115); and reproductive toxicity: 100.00 %, 48.57 % (n=246). CASE Ultra correctly recognized the relevant toxic effects in 43 out of the 47 withdrawn drugs. It predicted all 9 drugs that were not part of the training set of the models, as unsafe.

Optimizing predictive performance of CASE Ultra expert system models using the applicability domains of individual toxicity alerts.

Fragment based expert system models of toxicological end points are primarily comprised of a set of substructures that are statistically related to the toxic property in question. These special substructures are often referred to as toxicity alerts, toxicophores, or biophores. They are the main building blocks/classifying units of the model, and it is important to define the chemical structural space within which the alerts are expected to produce reliable predictions. Furthermore, defining an appropriate applicability domain is required as part of the OECD guidelines for the validation of quantitative structure-activity relationships (QSARs). In this respect, this paper describes a method to construct applicability domains for individual toxicity alerts that are part of the CASE Ultra expert system models. Defining applicability domain for individual alerts was necessary because each CASE Ultra model is comprised of multiple alerts, and different alerts of a model usually represent different toxicity mechanisms and cover different structural space; the use of an applicability domain for the overall model is often not adequate. The domain for each alert was constructed using a set of fragments that were found to be statistically related to the end point in question as opposed to using overall structural similarity or physicochemical properties. Use of the applicability domains in reducing false positive predictions is demonstrated. It is now possible to obtain ROC (receiver operating characteristic) profiles of CASE Ultra models by applying domain adherence cutoffs on the alerts identified in test chemicals. This helps in optimizing the performance of a model based on their true positive-false positive prediction trade-offs and reduce drastic effects on the predictive performance caused by the active/inactive ratio of the model's training set. None of the major currently available commercial expert systems for toxicity prediction offer the possibility to explore a model's full range of sensitivity-specificity spectrum, and therefore, the methodology developed in this study can be of benefit in improving the predictive ability of the alert based expert systems.

Benchmark performance of MultiCASE Inc. software in Ames mutagenicity set.

The predictive performances of MC4PC were evaluated using its learning machine functionality. Its superior characteristics are demonstrated in this following up study using the newly published Ames mutagenicity benchmark set.

Identification of structure-activity relationships for adverse effects of pharmaceuticals in humans: Part B. Use of (Q)SAR systems for early detection of drug-induced hepatobiliary and urinary tract toxicities.

This report describes the development of quantitative structure-activity relationship (QSAR) models for predicting rare drug-induced liver and urinary tract injury in humans based upon a database of post-marketing adverse effects (AEs) linked to approximately 1600 chemical structures. The models are based upon estimated population exposure using AE proportional reporting ratios. Models were constructed for 5 types of liver injury (liver enzyme disorders, cytotoxic injury, cholestasis and jaundice, bile duct disorders, gall bladder disorders) and 6 types of urinary tract injury (acute renal disorders, nephropathies, bladder disorders, kidney function tests, blood in urine, urolithiases). Identical training data sets were configured for 4 QSAR programs (MC4PC, MDL-QSAR, BioEpisteme, and Predictive Data Miner). Model performance was optimized and was shown to be affected by the AE scoring method and the ratio of the number of active to inactive drugs. The best QSAR models exhibited an overall average 92.4% coverage, 86.5% specificity and 39.3% sensitivity. The 4 QSAR programs were demonstrated to be complementary and enhanced performance was obtained by combining predictions from 2 programs (average 78.4% specificity, 56.2% sensitivity). Consensus predictions resulted in better performance as judged by both internal and external validation experiments.

A structural analysis of the differential cytotoxicity of chemicals in the NCI-60 cancer cell lines.

The primary functions of cancer chemotherapeutic agents are not only to inhibit the growth or kill the cancer cells, but to do so without eliciting unreasonable cytotoxic effects on the healthy cells and to withstand the ability of the cancer cells to develop resistance against it. This has unfortunately been proven so far to be a very difficult objective. In this perspective, the ability of small molecules (anti-tumor agents) to 'see' different cell types differently can be a key attribute. Thus the term 'differential cytotoxicity' is normally used to describe the drug's specificity. In the present paper, we have quantified differential cytotoxicity from a study of the chemicals tested in the National Cancer Institute's Developmental Therapeutics Program. The MULTICASE (Multiple Computer Automated Structure Evaluation) methodology was used to discover statistically significant structural fragments (biophores) related to the differential cytotoxicity of the compounds. We found that even small structural features often become important in this regard which is evident from the biophores that were found in structurally diverse chemicals. By utilizing the difference between the raw and normalized differential cytotoxicity indices, we found that the alpha,beta-unsaturated carbonyl group (O=C-C=CH(2)) is the major biophore associated with compounds with essentially parallel concentration profiles in the cell lines in question. These compounds have high non-normalized differential cytotoxicity but considerably low normalized differential cytotoxocity. The models developed were cross validated for their predictive ability.

MultiCASE Expert Systems and the REACH Initiative.

ABSTRACT This article is a review of the MultiCASE Inc. software and expert systems and their use to assess acute toxicity, mutagenicity, carcinogenicity, and other health effects. It is demonstrated that MultiCASE expert systems satisfy the guidelines of the Organisation for Economic Cooperation and Development (OECD) principles and that the portfolio of available endpoints closely overlaps with the list of tests required by REACH.

A comprehensive model for reproductive and developmental toxicity hazard identification: II. Construction of QSAR models to predict activities of untested chemicals.

This report describes the construction, optimization and validation of a battery of quantitative structure-activity relationship (QSAR) models to predict reproductive and developmental (reprotox) hazards of untested chemicals. These models run with MC4PC software to predict seven general reprotox classes: male and female reproductive toxicity, fetal dysmorphogenesis, functional toxicity, mortality, growth, and newborn behavioral toxicity. The reprotox QSARs incorporate a weight of evidence paradigm using rats, mice, and rabbit reprotox study data and are designed to identify trans-species reprotoxicants. The majority of the reprotox QSARs exhibit good predictive performance properties: high specificity (>80%), low false positives (<20%), significant receiver operating characteristic (ROC) values (>2.00), and high coverage (>80%) in 10% leave-many-out validation experiments. The QSARs are based on 627-2023 chemicals and exhibited a wide applicability domain for FDA regulated organic chemicals for which they were designed. Experiments were also performed using the MC4PC multiple module prediction technology, and ROC statistics, and adjustments to the ratio of active to inactive (A/I ratio) chemicals in training data sets were made to optimize the predictive performance of QSAR models. Results revealed that an A/I ratio of approximately 40% was optimal for MC4PC. We discuss specific recommendations for the application of the reprotox QSAR battery.

A structural analogue approach to the prediction of the octanol-water partition coefficient.

A new strategy for the calculation of n-octanol/water partition coefficients is presented. Log P calculations of unknown chemicals are based on their closest structural analogues from a database of molecules with known experimental log P values. The contribution of the differing molecular parts is then estimated from a compilation of fragment contributions. Such a strategy is found to be superior to conventional group contribution methods and promises an overall enhancement of the prediction's accuracy.

Recent methodologies for the estimation of n-octanol/water partition coefficients and their use in the prediction of membrane transport properties of drugs.

The lipophilicity of drug molecules (represented as the logarithm of the n-octanol/water partition coefficient) often strongly correlates with their pharmacological and toxic activities. It is therefore, not surprising that there is considerable interest in developing mathematical models capable to accurately predict their value for new drug candidates. In this review, current major approaches for estimating partition coefficients are described and some of their advantages and disadvantages are discussed. Recent uses of these partition coefficient algorithms in the development of membrane transport models are also discussed.

ESP: a method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases.

We describe here the development of a computer program which uses a new method called Expert System Prediction (ESP), to predict toxic end points and pharmacological properties of chemicals based on multiple modules created by the MCASE artificial intelligence system. The modules are generally based on different biological models measuring related end points. The purpose is to improve the decision making process regarding the overall activity or inactivity of the chemicals and also to enable rapid in silico screening. ESP evaluates the significance of the biophores from a different viewpoint and uses this information for predicting the activity of new chemicals. We have used a unique encoding system to represent relevant features of a chemical in the form of a pattern vector and a genetic artificial neural network (GA-ANN) to gain knowledge of the relationship between these patterns and the overall pharmacological property. The effectiveness of ESP is illustrated in the prediction of general carcinogenicity of a diverse set of chemicals using MCASE male/female rats and mice carcinogenicity modules.

MCASE study of the multidrug resistance reversal activity of propafenone analogs.

A database containing 130 propafenone type chemicals which have been tested for their multidrug resistance (MDR) reversal activity was compiled. Using the Multiple Computer-Automated Structure Evaluation (MCASE) program to analyze this database, an underlying relationship between MDR reversal activity and octanol/water partition coefficient was found. An MDR reversal model was created based on this database by the baseline activity identification algorithm (BAIA) of the MCASE program. The main phamacophores relevant to MDR reversal activity were identified.

Multiple computer-automated structure evaluation study of aquatic toxicity. III. Vibrio fischeri.

An acute toxicity model was constructed on the basis of 901 chemicals tested for toxicity against the luminescent bacteria Vibrio fischeri (formerly Photobacterium phosphoreum, the Microtox test). The model was created using the Multiple Computer-Automated Structure Evaluation (M-CASE) program. The model can correctly predict acute toxicity for 92% of the compounds with an error averaging 0.55 log units per median effect concentration (EC50). The main toxicophores, corresponding to polar and nonpolar narcosis, and other types of reactive chemicals were identified.

Structure-activity relationship study of a diverse set of estrogen receptor ligands (I) using MultiCASE expert system.

The MultiCASE expert system was used to construct a quantitative structure-activity relationship model to screen chemicals with estrogen receptor (ER) binding potential. Structures and ER binding data of 313 chemicals were used as inputs to train the expert system. The training data set covers inactive, weak as well as very powerful ER binders and represents a variety of chemical compounds. Substructural features associated with ER binding activity (biophores) and features that prevent receptor binding (biophobes) were identified. Although a single phenolic hydroxyl group was found to be the most important biophore responsible for the estrogenic activity of most of the chemicals, MultiCASE also identified other biophores and structural features that modulate the activity of the chemicals. Furthermore, the findings supported our previous hypothesis that a 6 A distant descriptor may describe a ligand-binding site on an ER. Quantitative structure-activity relationship models for the chemicals associated with each biophore were constructed as part of the expert system and can be used to predict the activity of new chemicals. The model was cross validated via 10 x 10%-off tests, giving an average concordance of 84.04%.

Screening of high production volume chemicals for estrogen receptor binding activity (II) by the MultiCASE expert system.

A structurally and functionally diverse and cross-validated quantitative structure-activity knowledge database generated by the MultiCASE expert system was used to screen 2526 high production volume chemicals (HPVCs) for their estrogen receptor binding activity. 73 HPVCs were found to contain structural features or biophores that have been documented as having the ability to bind to the estrogen receptor. Potential chemicals were ranked according to their quantitatively predicted ER binding potential and the details of the biophores found in them are discussed.

ADME evaluation. 2. A computer model for the prediction of intestinal absorption in humans.

PURPOSE: To develop a computational method to rapidly evaluate human intestinal absorption, one of the drug properties included in the term ADME (Absorption, Distribution, Metabolism, Excretion). Poor ADME properties are the most important reason for drug failure in clinical development. METHODS: The model developed is based on a modified contribution group method in which the basic parameters are structural descriptors identified by the CASE program, together with the number of hydrogen bond donors. RESULTS: The human intestinal absorption model is a quantitative structure-activity relationship (QSAR) that includes 37 structural descriptors derived from the chemical structures of a data set containing 417 drugs. The model was able to predict the percentage of drug absorbed from the gastrointestinal tract with an r2 of 0.79 and a standard deviation of 12.32% of the compounds from the training set. The standard deviation for an external test set (50 drugs) was 12.34%. CONCLUSIONS: The availability of reliable and fast models like the one we propose here to predict ADME/Tox properties could help speed up the process of finding compounds with improved properties, ultimately making the entire drug discovery process shorter and more cost efficient.

An MCASE approach to the search of a cure for Parkinson's Disease.

BACKGROUND: Parkinson's disease is caused by a dopamine deficiency state in the fore brain area. Dopamine receptor agonists, MAO-B inhibitors, and N-Methyl-D-Aspartate (NMDA) receptor antagonists are known to have antiparkinson effect. Levodopa, a dopamine structural analog, is the best currently available medication for the treatment of Parkinsons disease. Unfortunately, it also induces side effects upon long administration time. Thus, multidrug therapy is often used, in which various adjuvants alleviate side effects of levodopa and enhance its antiparkinsonian action. RESULTS: Computer models have been created for three known antiparkinson mechanisms using the MCASE methodology. New drugs for Parkinsons disease can be designed on the basis of these models. We also speculate that the presence of biophores belonging to different groups can be beneficial and designed some potential drugs along this line. The proposed compounds bear pharmacophores of MAO-B inhibitors, dopamine agonists and NMDA antagonists, which could synergistically enhance their antiparkinson effect. CONCLUSIONS: The methodology could readily be expanded to other endpoints where drugs with multiple activity mechanisms would be desirable.